So called personal digital assistants, such as the EO.TM. and Newton.TM. products, typically have a touch sensitive screen upon which a user can impose handwriting. These devices then function to digitize the handwritten character input. Other devices, which function to receive handwritten input include, but are not limited to the following: desktop computers, modems, pagers, advanced telephones, digital or interactive televisions, and other information processing devices having access to a digitizing tablet that can accept handwritten character input. Still other devices can receive handwritten character input by means of a facsimile or scanned input. .sub.-- These devices process the information to attempt to recognize the information content of the handwritten character input and display that information to the user for purposes of feedback and correction of errors in the processing and recognition of the handwritten character input.
Pursuant to another prior art approach, a dictionary is accessed and entries within the dictionary are compared against the initial handwriting analysis results. Using this approach, one seeks those entries within the dictionary that most closely fit the characteristics of the handwriting sample. For use with handwriting samples that represent information contained within the dictionary, this approach works reasonably well. Often, however, the handwriting input will not be in the dictionary. For example, proper names, geographic locations, acronyms, and professional jargon are typically not included within such dictionaries. Expanding the dictionary to include virtually all words and acronyms, on the other hand, presently constitutes an unsatisfactory solution, since the amount of memory required, and the computational overhead necessary to support a full search of such an extensive dictionary, all make this approach impractical.
Another problem associated with the prior art is the recognition of numeric handwritten input. Many numbers bear a strong resemblance to words that may be in the dictionary (for example, "15" may be easily confused with "is"). A dictionary based system will be unable to correctly identify "15" when written. Accordingly, a need exists for some method to allow this input to be identified correctly and presented to the user as a possible translation of the handwritten character input.
Another problem often associated with the prior art handwriting recognition techniques of the prior art is the format in which the digitized handwritten alphanumeric input is displayed to the user after the input has been analyzed. In particular, prior art methods for displaying the output are confusing when the output contains errors. In many cases, users cannot remember what they wrote and are unable to make sense of the errors in the output in order to correct them.
Accordingly, a need exists for a handwriting recognition technique that can avoid or minimize these limitations and at the same time present the information in a format which allows the user to correct any errors with direct reference to their intended handwritten input.